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  1. %% Created using Papers on Wed, 21 Dec 2016.
  2. %% http://papersapp.com/papers/
  3. @article{Taylor:uo,
  4. author = {Taylor, GW and Sigal, Leonid and Fleet, David J and Hinton, G},
  5. title = {{Dynamical Binary Latent Variable Models for 3D Human Pose Tracking}}
  6. }
  7. @inproceedings{Adams:2009vh,
  8. author = {Adams, Ryan P and Wallach, H and Ghahramani, Zoubin},
  9. title = {{Learning the Structure of Deep Sparse Graphical Models}},
  10. booktitle = {International Conference on Airtificial Inteligence and Statistical Learning},
  11. year = {2010}
  12. }
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  353. The solution that is proposed is quite straight-forward. Given a generative mapping then the most likely permutation can be solved by simply computing all the distances and run something like a Hungarian method to do the alignment. Once this is done, a distribution of permutations is created by sampling a few distributions that are close to the best one. This is motivated by [1] where the authors show that the distribution over permuations is actually very very peaked.
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  476. annote = {Good article that details and connects the notion of similarity and how one would go about modelling this. The most important part is that they raise the fact that it is not clear that we should perceive similarity as something related to distance. A good example that is provided is that people apparently think that North Korea is more similar to China than China is to North Korea, modelling this notion cannot be done with a distance measure.},
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  767. Its easy to append the system with a feed-back loop which takes into account the current position.
  768. Experiments shows that for a human reaching task the CP trained for left side can be used for reaching at different heights. However, different CP's are needed for left and right side of the body.
  769. Thoughts:
  770. The model is rather simple and a CP is trained from what I understand from a single trajectory. It requires discrete movements i.e. the path need to end with zero velocity. It could potentially be interesting to think of an extension where one would use an MLP kernel instead which would allow for a non-discrete movements.}
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  1368. title = {{Categorizing Object-Action Relations from Semantic Scene Graphs}},
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  1372. annote = {The paper is an approach of how to relate manipulation to the manipulated objects. The point is that we as human do the "same thing" in vastly different ways.
  1373. The approach is to first apply a segmentation algorithm to a video sequence. When this has been done a clustering approach labels and finds temporally consistent segments througout the video. These segments corresponds to nodes in a scene-graph where the vertices connects nodes spatially neighbouring nodes. These graphs are refered to as "Semantic Scene Graphs".
  1374. From the Semantic Scene Graphs a second graph is extracted referred to as "Main Graph". This is done by applying a graph matching method and detecting topological changes in the graph over time and segmenting the sequence into these topologically graphs.
  1375. From the Main Graph and "Event Table" is created. The event table describes the interaction between every pair of nodes into four different classes:
  1376. <b>Absence:</b> one of the nodes are not appearing in the graph
  1377. <b>No Connection:</b> there is no vertice between the nodes
  1378. <b>Overlapping:</b> One of the nodes is completely sourounded by the other, i.e. it has only one vertice which goes to the sourounding node.
  1379. <b>Touching:</b> The nodes are connected but both also have vertices to other nodes.
  1380. <b>Task Classification:</b>
  1381. Given two different Event Tables their similarity can be computed by comparing them row by row. The final similarity measure is the mean of the highest score along each row.
  1382. <b>Object Categorization:</b>
  1383. Given a event graph with known node correspondences (i.e. node is sandwhich etc.) given a new event graph we can use an hungarian matching strategy in order to find the nodes corresponding.
  1384. <b>Thoughts:</b>
  1385. The paper is really good and interesting. The vision system is very rudimentary but that is also stated in the paper and it is also not the goal. There are clear benifits of not having a model when doing the types of tasks as suggested, however it is not clear how to generate new data using this. It is also interesting to see how this will extend to more complex interactions.
  1386. Further, is interaction between objects the only interesting thing? }
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  2026. The paper is written in the form of dimensionality reduction so for unsupervised learning. However, the method is more general for GPs with missing inputs.
  2027. 1. The first step is to formulate the likelihood of the observed data p(Y) = int p(Y|X)p(X) dX which is intractable to compute. However, by introducing a factorised approximation to X a lower bound can be formulated. This lower bound contains two parts, one which contains a marginalisation over X of p(y|X). This integral is intractable.
  2028. 2. The second step is to introduce an augumented likelihood which uses inducing points. By introducing an independence between the input locations of the observed data and the inducing points a variational lower bound on p(y|X) can be computed. The key idea here is that the locations of the inducing variables are not random variables but rather variational parameters that we can optimise.
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  3363. annote = {The paper learns a face normalization mapping from non-frontal poses to a frontal pose from only 2D spatial observations of the face.
  3364. In the first stage the pose space is discretised into a set of facial expressions. Given that these same expressions are observed in several different poses the aim is to learn a mapping from the non-frontal poses to the frontal. This is referred to as pose-normalisation.
  3365. 1. Learn through LDA a class respecting representation of the faces. This model gives the conditional likelihood of a face belonging to a specific expression.
  3366. 2. Learn a GP that maps the faces from each cathegory in the different poses to the frontal pose
  3367. As the mappings are assumed to be coupled, i.e. there is clearly a lot of shared information in the final step this coupling is learned.
  3368. 3. By assuming that the dependence between the mappings can be modeled as a Gaussian the variance between two estimations are learned. This additional term can be seen as an extra noise term when joining the mappings by adding the noise of the coupling to the GP noise learned through regression.
  3369. A heuristic method is used to decide what combination of predictors should be used for the final estimate.
  3370. Conclusion:
  3371. The paper is quite nice, however its unprincipled in one way as it assumes the mappings to be independent when it learns them and then afterwards adds this additional coupling. The strange thing is that by coupling of the mappings you will always increase the uncertainty in the predictions.}
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  4280. author = {Robert, Christian P},
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  4289. title = {{Modular Learning in Neural Networks}},
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  4292. annote = {What the title says. Talks about the problem of learning in Neural networks and especially for autoassociative models. The solution is to have a modular learning scheme.}
  4293. }
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  4295. author = {Malisiewicz, Tomasz and Gupta, Abhinav and Efros, Alexei A.},
  4296. title = {{Ensemble of Exemplar-SVMs for Object Detection and Beyond}},
  4297. booktitle = {International Conference on Computer Vision},
  4298. year = {2011},
  4299. organization = {Carnegie Mellon University},
  4300. address = {Barcelona},
  4301. annote = {The paper proposes a very simple idea, rather than training a classifier per class it instead takes the approach of creating something inbetween a NN and a generalized classifier. To do so the authors learn a single classifier per example in the data. The intuition is to learn the best separation in terms of each single example. This creates a non-parametric version of a parametric classifier in a neat manner as it will not require the positive examples to be represented using the same parametrisation.
  4302. Classic Efros paper, neat idea that works really well with some good intuitions in there. My main question remains who the classifier are merged into one single output result.}
  4303. }
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  4365. title = {{Extracting Postural Synergies for Robotic Grasping}},
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  4378. }},
  4379. journal = {IEEE Transaction on Robotics}
  4380. }
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  4400. journal = {International Journal of Robotic Research},
  4401. year = {2013},
  4402. month = jun
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  4410. title = {{Efficient Bayesian Inference for Generalized Bradley{\textendash}Terry Models}},
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  4419. author = {Cottrell, Garrison W and Munro, Paul and Zipser, David},
  4420. title = {{Learning internal representations from gray-scale images: An example of extensional programming}},
  4421. journal = {Ninth annual conference of the cognitive science society},
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  4433. url = {http://users.soe.ucsc.edu/~manfred/pubs/C78.pdf}
  4434. }
  4435. @incollection{Rojas:1996ws,
  4436. author = {Rojas, Rau l},
  4437. title = {{Associative Networks}},
  4438. booktitle = {Neural Networks: A Systematic Introduction},
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  4442. address = {Berlin}
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  4445. author = {Figueiredo, MAT and Nowak, RD and Wright, SJ},
  4446. title = {{Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems}},
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  4460. pages = {613--620},
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  4462. annote = {Use this as a reference for Bag-of-Words models.}
  4463. }
  4464. @webpage{Anonymous:53PiX0eh
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  4466. @inproceedings{Collins:2001uh,
  4467. author = {Collins, M and Duffy, N},
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  4488. }
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  4491. title = {{Gaussian Mixture Modeling with Gaussian Process Latent Variable Models}},
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  4495. }
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  4499. }
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  4545. journal = {ACM Transactions on Graphics (TOG)},
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  4568. title = {{Variational Inference}},
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  4580. @phdthesis{kadous2002temporal,
  4581. author = {Kadous, M W},
  4582. title = {{Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series}},
  4583. school = {University of New South Wales},
  4584. year = {2002}
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  4586. @article{Spearman:1904tx,
  4587. author = {Spearman, Charles},
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  4597. title = {{On approximate string matching}},
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  6226. title = {{Annual Research Review: Infant development, autism, and ADHD - early pathways to emerging disorders.}},
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  6280. annote = {Important paper that describes from a philosophical point of view what factor analysis and latent variable models is actually all about. Very interesting stuff and puts names on things that I knew intuitively.}
  6281. }
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  6337. author = {Memisevic, Roland and Sigal, Leonid and Fleet, David J},
  6338. title = {{Shared kernel information embedding for discriminative inference}},
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  6346. author = {Nakamura, Tomoaki and Nagai, Takayuki},
  6347. title = {{Object Concept Modeling Based on the Relationship among Appearance, Usage and Functions}},
  6348. journal = {International Conference of Inteligent Robots and Systems},
  6349. year = {2010},
  6350. pages = {1--6},
  6351. month = aug,
  6352. annote = {The paper presents a framework for modeling object interaction. The idea is to try to understand the object which is coined as: preditction of its function and usage from apperance. In order to do so a Bayesian framework is used.
  6353. 1. A complete model which connect Usage, Function and apperance is built and structured by hand.
  6354. 2. Each model is learned independently. Where there for function and usage is a discrete latent variable with a dirchlet prior which modeles the function and usage class. The number of classes are assumed to have a uniform prior and can be learned from data.
  6355. 3. When each independent model have been trained inference over the different cathegories can be done.
  6356. The paper is very interesting as it is using real observations and not artificial data. However, the structure of the model is defined a-priori which is not ideal.}
  6357. }
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  6437. title = {{Control, Planning, Learning and Imitation with Dynamic Movement Primitives}},
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  6459. publisher = {Springer Berlin Heidelberg},
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  6510. title = {{The ́orie Analytique des Probabilite ́s}},
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  6525. title = {{Spatio-Temporal Modeling of Grasping Actions}},
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  6535. }
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  6548. title = {{The Hopfield Model}},
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  6553. address = {Berlin}
  6554. }
  6555. @inproceedings{Gabiccini:wa,
  6556. author = {Gabiccini, Marco and Bicchi, Antonio},
  6557. title = {{On the Role of Hand Synergies in the Optimal Choice of Grasping Forces}},
  6558. booktitle = {Robotics Science and Systems},
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  6560. organization = { Dept. of Mechanical, Nuclear and Production Eng. University of Pisa},
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  6563. @inproceedings{Damianou:2011vb,
  6564. author = {Damianou, Andreas C and Titsias, Michalis and Lawrence, Neil D},
  6565. title = {{Variational Gaussian Process Dynamical Systems}},
  6566. booktitle = {Advances in Neural Information Processing Systems},
  6567. year = {2011},
  6568. organization = {University of Sheffield, University of Manchester},
  6569. address = {Granda},
  6570. month = dec,
  6571. annote = {This paper allows for a prior over the latent space to be marginalised out where the latent locations are correlated.
  6572. It is based on the variational trick by specifying an agumented model using inducing points. However, the big difference is that it allows for correlations between the latent locations.}
  6573. }
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  6575. title = {{ARM assembler in Raspberry Pi {\textendash} Chapter 10}},
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  6580. @article{Plis:2010vw,
  6581. author = {Plis, Sergey M and Lane, Terran and Calhoun, Vince D},
  6582. title = {{Directional Statistics on Permutations}},
  6583. journal = {arXiv.org},
  6584. year = {2010},
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  6586. eprinttype = {arxiv},
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  6590. @article{Santello:1998un,
  6591. author = {Santello, M and Flanders, Martha},
  6592. title = {{Postural hand synergies for tool use}},
  6593. journal = {The Journal of Neuroscience},
  6594. year = {1998},
  6595. annote = {This paper analyses a large database of different grasps from several different persons applied to a large set of objects. The analysis is centred around trying to get a notion of the effective degrees of freedom in hand motion. The database is analysed using PCA in order to study the variance in the data. In the paper it is shown that the major part of the variance is explained by the first few eigenmodes in the data. However, as all the analysis is based on PCA only the linear variance is extracted, further it is shown that the higher order components contains signal and not just random noise.
  6596. The paper is a good study of PCA applied to hands, however, it is limited by only studying this using PCA as it is quite likely that the data has non-linear correlations. Further, as reconstruction error in the jointspace is reported as an error measure this assumes that the joint space representation is "smooth" in terms of grasping which is not necessarily true.}
  6597. }
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  6607. title = {{The maximum-likelihood solution in inter-battery factor analysis}},
  6608. journal = {British Journal of Mathematical and Statistical Psychology},
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  6615. @article{Dai:2015us,
  6616. author = {Dai, Zhenwen and Hensman, James and Lawrence, Neil D},
  6617. title = {{Spike and Slab Gaussian Process Latent Variable Models}},
  6618. journal = {arXiv.org},
  6619. year = {2015},
  6620. eprint = {1505.02434v1},
  6621. eprinttype = {arxiv},
  6622. eprintclass = {stat.ML},
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  6626. author = {Quattoni, Ariadna and Carreras, Xavier and Collins, Michael and Darrell, Trevor},
  6627. title = {{An efficient projection for l1, $\infty$ regularization}},
  6628. booktitle = {International Conference on Machine Learning},
  6629. year = {2009},
  6630. publisher = {~ACM},
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  6633. @article{Ciocarlie:2010eo,
  6634. author = {Ciocarlie, M. and Allen, P.},
  6635. title = {{Data-driven optimization for underactuated robotic hands}},
  6636. journal = {International Conference on Robotics and Automation},
  6637. year = {2010},
  6638. pages = {1292--1299}
  6639. }
  6640. @inproceedings{Gormley:uo,
  6641. author = {Gormley, M R and Dredze, M and Van Durme, B and Eisner, J},
  6642. title = {{Shared Components Topic Models}},
  6643. booktitle = {IEEE-RAS International Conference on Humanoid Robotics},
  6644. year = {2012},
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  6646. }
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  6648. author = {Klami, Arto and Virtanen, Seppo and Leppaaho, Eemeli and Kaski, Samuel},
  6649. title = {{Group Factor Analysis}},
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  6651. year = {2014},
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  6655. }
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  6657. author = {Schaal, Stefan and Sternad, D and Atkeson, CG},
  6658. title = {{One-handed juggling: A dynamical approach to a rhythmic movement task}},
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  6665. @inproceedings{Bitzer:2009tq,
  6666. author = {Bitzer, S and Vijayakumar, Sethu},
  6667. title = {{Latent Spaces for Dynamic Movement Primitives}},
  6668. booktitle = {IEEE-RAS International Conference on Humanoid Robotics},
  6669. year = {2009},
  6670. month = jan,
  6671. annote = {Rather then using the standard approach of training a DMP model from the raw motion data this paper suggest that prior to learning the DMP apply a dimensionality reduction model to the data. Having done so the DMP is learned in the latent representation of the data.
  6672. In this paper the dimensionality reduction model of choice is the GPLVM. However, as shown in the paper, in order to make the DMP model work well the latent space needs to be regularized. This is done by adding one additonal term to the GPLVM which penalizes latent representations of sequences that diverges from a simple template sequence. This in order to better fit the DMP. This model is refered to as the "SS-GPLVM".
  6673. The paper show promising results where the correlations between different joints are handled by learning a new representation and where the DMP can create new novel sequences.
  6674. <b>Comment:</b>
  6675. The paper is nice, however, in all of the experiments the model is applied to very simple data with only a few elements. This is the classical example where the GPLVM works really well to interpolate between really sparse data. In none of the examples one tries to extrapolate outside the training data.
  6676. However, I am unsure of the sougt applications, do we know what type of motion that we are interested in generating? If so we might have a multitude of different DMP's that we can apply and in such case it is very likely that this model will perform quite well.
  6677. <b>Extensions:</b>
  6678. One could possibly alter the auto-regressive GP to get similar results in a more principled way, both by balancing and by using a much simpler auto-regressive model with better correspondence to the DMP.}
  6679. }
  6680. @inproceedings{Sriperumbudur:2010ta,
  6681. author = {Sriperumbudur, Bharath K and Lanckriet, Gert R G},
  6682. title = {{Metric Embedding for Nearest Neighbor Classification}},
  6683. booktitle = {arXiv.org},
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  6686. }
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  6688. author = {Hensman, James and Rattray, Magnus and Lawrence, Neil D},
  6689. title = {{Fast Variational Inference in the Conjugate Exponential Family.}},
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  6697. title = {{Convolution kernels on discrete structures}},
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  6704. }
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  6707. title = {{Learning from demonstration}},
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  8569. The system is non-linearised by using a set of kernel functions (Gaussians). In the dynamical system. Fitting the model to data means learning the weights of this regression function which is done independently for each basis/kernel function. I.e. using a locally weighted regression scheme (LWR).
  8570. When a CP is fitted to real data the paper shows that a CP fitted to a specific movemement is flexible enough to well model similar movements.
  8571. One of the advantages of the model is that by scaling the parameters the trajectories can be modulated in time. This means that its easy to correct misstakes and alter and extend motions.
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  8816. annote = {Actually a lot more straight forward than I expected. Compared to the original dynamic GP-LVM where we used an autoregressive model on the latent space the approach is now given the bayesian treatment to model this using a GP. This means that there is a covariance function modelling the correlations between points on the latent space.}
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  8848. author = {Rusu, Radu Bogdan},
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  8850. school = {Technische Universit{\"a}t M{\"u}nchen},
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  8927. title = {{Learning a Gaussian Process Prior for Automatically Generating Music Playlists}},
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  8932. @inproceedings{kajiya1986rendering,
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  8945. journal = {Trans. Evol. Comp},
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  8955. doi = {10.1109/4235.585893},
  8956. acmid = {2221408},
  8957. publisher = {IEEE Press},
  8958. address = {Piscataway, NJ, USA},
  8959. }
  8960. @INPROCEEDINGS{Boyle05dependentgaussian,
  8961. author = {Phillip Boyle and Marcus Frean},
  8962. title = {Dependent Gaussian processes},
  8963. booktitle = {In Advances in Neural Information Processing Systems 17},
  8964. year = {2005},
  8965. pages = {217--224},
  8966. publisher = {MIT Press}
  8967. }
  8968. @ARTICLE{2016_ZhouDeLaTorre,
  8969. author={F. Zhou and F. De la Torre},
  8970. journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  8971. title={Generalized Canonical Time Warping},
  8972. year={2016},
  8973. volume={38},
  8974. number={2},
  8975. pages={279-294},
  8976. keywords={correlation methods;image sequences;motion estimation;video surveillance;CCA;DTW;GCTW;activity recognition;canonical correlation analysis;dynamic time warping;generalized canonical time warping;human motion temporal alignment;motion capture data;multimodal sequences;tele-rehabilitation;Complexity theory;Computer graphics;Computer vision;Correlation;Manifolds;Optimization;Time series analysis;Canonical correlation analysis;Dynamic time warping;Multi-modal sequence alignment;canonical correlation analysis;dynamic time warping},
  8977. doi={10.1109/TPAMI.2015.2414429},
  8978. ISSN={0162-8828},
  8979. month={Feb},}
  8980. @inproceedings{Goral:1984:MIL:800031.808601,
  8981. author = {Goral, Cindy M. and Torrance, Kenneth E. and Greenberg, Donald P. and Battaile, Bennett},
  8982. title = {Modeling the Interaction of Light Between Diffuse Surfaces},
  8983. booktitle = {Proceedings of the 11th Annual Conference on Computer Graphics and Interactive Techniques},
  8984. series = {SIGGRAPH '84},
  8985. year = {1984},
  8986. isbn = {0-89791-138-5},
  8987. pages = {213--222},
  8988. numpages = {10},
  8989. url = {http://doi.acm.org/10.1145/800031.808601},
  8990. doi = {10.1145/800031.808601},
  8991. acmid = {808601},
  8992. publisher = {ACM},
  8993. address = {New York, NY, USA},
  8994. keywords = {Diffuse reflections, Form factors, Light reflection models, Radiosity, Shading},
  8995. }
  8996. @incollection{2011:Duvenaud,
  8997. title = {Additive Gaussian Processes},
  8998. author = {Duvenaud, David K and Hannes Nickisch and Carl E. Rasmussen},
  8999. booktitle = {Advances in Neural Information Processing Systems 24},
  9000. editor = {J. Shawe-Taylor and R. S. Zemel and P. L. Bartlett and F. Pereira and K. Q. Weinberger},
  9001. pages = {226--234},
  9002. year = {2011},
  9003. publisher = {Curran Associates, Inc.},
  9004. url = {http://papers.nips.cc/paper/4221-additive-gaussian-processes.pdf}
  9005. }
  9006. @InProceedings{pmlr-v48-hwangb16,
  9007. title = {Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series},
  9008. author = {Yunseong Hwang and Anh Tong and Jaesik Choi},
  9009. booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
  9010. pages = {3030--3039},
  9011. year = {2016},
  9012. editor = {Maria Florina Balcan and Kilian Q. Weinberger},
  9013. volume = {48},
  9014. series = {Proceedings of Machine Learning Research},
  9015. address = {New York, New York, USA},
  9016. month = {20--22 Jun},
  9017. publisher = {PMLR},
  9018. pdf = {http://proceedings.mlr.press/v48/hwangb16.pdf},
  9019. url = {http://proceedings.mlr.press/v48/hwangb16.html},
  9020. abstract = {Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions. The Automatic Bayesian Covariance Discovery (ABCD) system constructs natural-language description of time-series data by treating unknown time-series data nonparametrically using GP with a composite covariance kernel function. Unfortunately, learning a composite covariance kernel with a single time-series data set often results in less informative kernel that may not give qualitative, distinctive descriptions of data. We address this challenge by proposing two relational kernel learning methods which can model multiple time-series data sets by finding common, shared causes of changes. We show that the relational kernel learning methods find more accurate models for regression problems on several real-world data sets; US stock data, US house price index data and currency exchange rate data.}
  9021. }
  9022. @INPROCEEDINGS{Lloyd2014-ABCD,
  9023. title = "Automatic Construction and {Natural-Language} Description of
  9024. Nonparametric Regression Models",
  9025. author = {Lloyd, James Robert and Duvenaud, David and Grosse, Roger
  9026. and Tenenbaum, Joshua B. and Ghahramani, Zoubin},
  9027. booktitle = "Association for the Advancement of Artificial Intelligence
  9028. (AAAI)",
  9029. year = 2014
  9030. }
  9031. @inproceedings{DuvLloGroetal13,
  9032. title={Structure Discovery in Nonparametric Regression through Compositional Kernel Search},
  9033. author={David Duvenaud and James Robert Lloyd and Roger Grosse and Joshua B. Tenenbaum and Zoubin Ghahramani},
  9034. institution = {Department of Engineering, University of Cambridge},
  9035. year={2013},
  9036. month={June},
  9037. booktitle={Proceedings of the 30th International Conference on Machine Learning},
  9038. }
  9039. @inproceedings{Lazaro-GredillaT11,
  9040. Author = {Miguel L{\'{a}}zaro{-}Gredilla and
  9041. Michalis K. Titsias},
  9042. editor = {Lise Getoor and
  9043. Tobias Scheffer},
  9044. title = {Variational Heteroscedastic Gaussian Process Regression},
  9045. booktitle = {Proceedings of the 28th International Conference on Machine Learning,
  9046. {ICML} 2011, Bellevue, Washington, USA, June 28 - July 2, 2011},
  9047. pages = {841--848},
  9048. publisher = {Omnipress},
  9049. year = {2011},
  9050. timestamp = {Wed, 29 Mar 2017 16:45:25 +0200},
  9051. biburl = {http://dblp.uni-trier.de/rec/bib/conf/icml/Lazaro-GredillaT11},
  9052. bibsource = {dblp computer science bibliography, http://dblp.org}
  9053. }
  9054. @incollection{NIPS2007_3366,
  9055. title = {Modeling Natural Sounds with Modulation Cascade Processes},
  9056. author = {Richard Turner and Sahani, Maneesh},
  9057. booktitle = {Advances in Neural Information Processing Systems 20},
  9058. editor = {J. C. Platt and D. Koller and Y. Singer and S. T. Roweis},
  9059. pages = {1545--1552},
  9060. year = {2008},
  9061. publisher = {Curran Associates, Inc.},
  9062. url = {http://papers.nips.cc/paper/3366-modeling-natural-sounds-with-modulation-cascade-processes.pdf}
  9063. }
  9064. @inproceedings{DBLP:conf/icml/AdamsS08,
  9065. author = {Ryan Prescott Adams and
  9066. Oliver Stegle},
  9067. editor = {William W. Cohen and
  9068. Andrew McCallum and
  9069. Sam T. Roweis},
  9070. title = {Gaussian process product models for nonparametric nonstationarity},
  9071. booktitle = {Machine Learning, Proceedings of the Twenty-Fifth International Conference
  9072. {(ICML} 2008), Helsinki, Finland, June 5-9, 2008},
  9073. series = {{ACM} International Conference Proceeding Series},
  9074. volume = {307},
  9075. pages = {1--8},
  9076. publisher = {{ACM}},
  9077. year = {2008},
  9078. url = {http://doi.acm.org/10.1145/1390156.1390157},
  9079. doi = {10.1145/1390156.1390157},
  9080. timestamp = {Thu, 14 Aug 2008 13:58:39 +0200},
  9081. biburl = {http://dblp.uni-trier.de/rec/bib/conf/icml/AdamsS08},
  9082. bibsource = {dblp computer science bibliography, http://dblp.org}
  9083. }
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  9085. author = {Marc Peter Deisenroth and
  9086. Jun Wei Ng},
  9087. editor = {Francis R. Bach and
  9088. David M. Blei},
  9089. title = {Distributed Gaussian Processes},
  9090. booktitle = {Proceedings of the 32nd International Conference on Machine Learning,
  9091. {ICML} 2015, Lille, France, 6-11 July 2015},
  9092. series = {{JMLR} Workshop and Conference Proceedings},
  9093. volume = {37},
  9094. pages = {1481--1490},
  9095. publisher = {JMLR.org},
  9096. year = {2015},
  9097. url = {http://jmlr.org/proceedings/papers/v37/deisenroth15.html},
  9098. timestamp = {Wed, 29 Mar 2017 16:45:25 +0200},
  9099. biburl = {http://dblp.uni-trier.de/rec/bib/conf/icml/DeisenrothN15},
  9100. bibsource = {dblp computer science bibliography, http://dblp.org}
  9101. }
  9102. @article{DBLP:journals/jmlr/CandelaR05,
  9103. author = {Joaquin Qui{\~{n}}onero Candela and
  9104. Carl Edward Rasmussen},
  9105. title = {A Unifying View of Sparse Approximate Gaussian Process Regression},
  9106. journal = {Journal of Machine Learning Research},
  9107. volume = {6},
  9108. pages = {1939--1959},
  9109. year = {2005},
  9110. url = {http://www.jmlr.org/papers/v6/quinonero-candela05a.html},
  9111. timestamp = {Wed, 21 Feb 2007 09:15:37 +0100},
  9112. biburl = {http://dblp.uni-trier.de/rec/bib/journals/jmlr/CandelaR05},
  9113. bibsource = {dblp computer science bibliography, http://dblp.org}
  9114. }
  9115. @InProceedings{Kingma:2014:aevb,
  9116. Title = {Auto-Encoding Variational {Bayes}},
  9117. Author = {Kingma, D. P. and Welling, M.},
  9118. Booktitle = {Proceedings of the International Conference on Learning Representations},
  9119. Year = {2014},
  9120. }
  9121. @proceedings{DBLP:conf/cvpr/2012,
  9122. title = {2012 {IEEE} Conference on Computer Vision and Pattern Recognition,
  9123. Providence, RI, USA, June 16-21, 2012},
  9124. publisher = {{IEEE} Computer Society},
  9125. year = {2012},
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  9127. isbn = {978-1-4673-1226-4},
  9128. timestamp = {Tue, 25 Nov 2014 17:05:17 +0100},
  9129. biburl = {http://dblp.dagstuhl.de/rec/bib/conf/cvpr/2012},
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  9131. }
  9132. @Article{DBLP:journals/pami/NicolaouPP14,
  9133. author = {Mihalis A. Nicolaou and Vladimir Pavlovic and Maja
  9134. Pantic},
  9135. title = {Dynamic Probabilistic {CCA} for Analysis of
  9136. Affective Behavior and Fusion of Continuous
  9137. Annotations},
  9138. year = 2014,
  9139. volume = 36,
  9140. number = 7,
  9141. pages = {1299-1311},
  9142. doi = {10.1109/TPAMI.2014.16},
  9143. url = {https://doi.org/10.1109/TPAMI.2014.16},
  9144. journal = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
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  9149. }
  9150. @InProceedings{DBLP:conf/isvc/EleftheriadisRP14,
  9151. author = {Stefanos Eleftheriadis and Ognjen Rudovic and Maja
  9152. Pantic},
  9153. title = {View-Constrained Latent Variable Model for
  9154. Multi-view Facial Expression Classification},
  9155. year = 2014,
  9156. booktitle = {Advances in Visual Computing - 10th International
  9157. Symposium, {ISVC} 2014, Las Vegas, NV, USA, December
  9158. 8-10, 2014, Proceedings, Part {II}},
  9159. pages = {292-303},
  9160. doi = {10.1007/978-3-319-14364-4_28},
  9161. url = {https://doi.org/10.1007/978-3-319-14364-4_28},
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  9167. }
  9168. @proceedings{DBLP:conf/isvc/2014-2,
  9169. editor = {George Bebis and
  9170. Richard Boyle and
  9171. Bahram Parvin and
  9172. Darko Koracin and
  9173. Ryan McMahan and
  9174. Jason Jerald and
  9175. Hui Zhang and
  9176. Steven M. Drucker and
  9177. Chandra Kambhamettu and
  9178. Maha El Choubassi and
  9179. Zhigang Deng and
  9180. Mark Carlson},
  9181. title = {Advances in Visual Computing - 10th International Symposium, {ISVC}
  9182. 2014, Las Vegas, NV, USA, December 8-10, 2014, Proceedings, Part {II}},
  9183. series = {Lecture Notes in Computer Science},
  9184. volume = {8888},
  9185. publisher = {Springer},
  9186. year = {2014},
  9187. url = {https://doi.org/10.1007/978-3-319-14364-4},
  9188. doi = {10.1007/978-3-319-14364-4},
  9189. isbn = {978-3-319-14363-7},
  9190. timestamp = {Fri, 19 May 2017 01:26:34 +0200},
  9191. biburl = {http://dblp.dagstuhl.de/rec/bib/conf/isvc/2014-2},
  9192. bibsource = {dblp computer science bibliography, http://dblp.org}
  9193. }
  9194. @Article{DBLP:journals/tip/EleftheriadisRP15,
  9195. author = {Stefanos Eleftheriadis and Ognjen Rudovic and Maja
  9196. Pantic},
  9197. title = {Discriminative Shared Gaussian Processes for
  9198. Multiview and View-Invariant Facial Expression
  9199. Recognition},
  9200. year = 2015,
  9201. volume = 24,
  9202. number = 1,
  9203. pages = {189-204},
  9204. doi = {10.1109/TIP.2014.2375634},
  9205. url = {https://doi.org/10.1109/TIP.2014.2375634},
  9206. journal = {{IEEE} Trans. Image Processing},
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  9210. bibsource = {dblp computer science bibliography, http://dblp.org}
  9211. }
  9212. @article{CONTI201431,
  9213. title = "Bayesian exploratory factor analysis",
  9214. journal = "Journal of Econometrics",
  9215. volume = "183",
  9216. number = "1",
  9217. pages = "31 - 57",
  9218. year = "2014",
  9219. note = "Internally Consistent Modeling, Aggregation, Inference and Policy",
  9220. issn = "0304-4076",
  9221. doi = "http://dx.doi.org/10.1016/j.jeconom.2014.06.008",
  9222. url = "http://www.sciencedirect.com/science/article/pii/S0304407614001493",
  9223. author = "Gabriella Conti and Sylvia Frühwirth-Schnatter and James J. Heckman and Rémi Piatek",
  9224. keywords = "Bayesian factor models",
  9225. keywords = "Exploratory factor analysis",
  9226. keywords = "Identifiability",
  9227. keywords = "Marginal data augmentation",
  9228. keywords = "Model expansion",
  9229. keywords = "Model selection"
  9230. }
  9231. @article{DBLP:journals/jmlr/Tipping01,
  9232. author = {Michael E. Tipping},
  9233. title = {Sparse Bayesian Learning and the Relevance Vector Machine},
  9234. journal = {Journal of Machine Learning Research},
  9235. volume = {1},
  9236. pages = {211--244},
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  9467. number={},
  9468. pages={491-496},
  9469. keywords={Clustering algorithms;Computer bugs;Detection algorithms;Hardware;Machine learning algorithms;Time measurement;Training data},
  9470. doi={10.7873/DATE.2013.112},
  9471. ISSN={1530-1591},
  9472. month={March},}